Exploring Trust in Self-Driving Vehicles Through Text Analysis

OBJECTIVE: This study examined attitudes toward self-driving vehicles and the factors motivating those attitudes. BACKGROUND: Self-driving vehicles represent potentially transformative technology, but achieving this potential depends on consumers' attitudes. Ratings from surveys estimate these attitudes, and open-ended comments provide an opportunity to understand their basis. METHOD: , which included a rating for level of trust with self-driving vehicles and associated open-ended comments. These open-ended comments are qualitative data that can be analyzed quantitatively using structural topic modeling. Structural topic modeling identifies common themes, extracts prototypical comments for each theme, and assesses how the survey year and rating affect the prevalence of these themes. RESULTS: Structural topic modeling identified 13 topics, such as "Tested for a long time," which was strongly associated with positive ratings, and "Hacking & glitches," which was strongly associated with negative ratings. The topics of "Self-driving accidents" and "Trust when mature" were more prominent in 2017 compared with 2016. CONCLUSION: Structural topic modeling reveals reasons underlying consumer attitudes toward vehicle automation. These reasons align with elements typically associated with trust in automation, as well as elements that mediate perceived risk, such as the desire for control as well as societal, relational, and experiential bases of trust. APPLICATION: The analysis informs the debate concerning how safe is safe enough for automated vehicles and provides initial indicators of what makes such vehicles feel safe and trusted.